Deepseek Coder 1.3B Base — Hardware Requirements & GPU Compatibility
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Deepseek Coder 1.3B Base is a 1.3B-parameter open language model from DeepSeek in the DeepSeek Coder family. It supports a context window of up to 16,384 tokens. At Q4_K_M it needs about 1.48 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~443 tok/s · 16K)Macs from 8 GB (Q4_K_M · ~48 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Deepseek Coder 1.3B Base
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run deepseek-coder:1.3b-base-q4_K_MQ4_K_M fits fully on the RTX 4090 · 19.7 GB free · 16K context
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (7 GB headroom) · Q4_K_M
- Generation speed
- ~401 tok/s
- generation speed
- Cost per 1M output tokens
- $0.085
- per 1M output tokens
How Much VRAM Does Deepseek Coder 1.3B Base Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.3 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 16K context · ~520 tok/s | |
| 1.3 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 16K context · ~489 tok/s | |
| 1.5 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 16K context · ~443 tok/s | |
| 1.6 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 16K context · ~402 tok/s | |
| 1.8 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 16K context · ~368 tok/s | |
| 2 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 16K context · ~328 tok/s | |
| 3.3 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 16K context · ~199 tok/s |
Selected Q4_K_M
4-bit medium quantization4.3GBat 16K context
- Weights
- 0.8 GB
- KV cache
- 3.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 19.7 GB free · ~443 tok/s
≥50 tok/s needs ≥114 GB/s · RTX 5090 ~787, RTX 3090 Ti ~443
Which Hardware Can Run Deepseek Coder 1.3B Base?
Q4_K_M · 1.5 GBDeepseek Coder 1.3B Base (Q4_K_M) requires 1.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 16K context window can add up to 2.8 GB, bringing total usage to 4.3 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Deepseek Coder 1.3B Base, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 3.7 GB free · up to 16K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~334Simulate output on RTX 3080RTX 2080 Ti~271Simulate output on RTX 2080 TiRTX 3070 Ti~267Simulate output on RTX 3070 TiM3*8 GB~48Simulate output on iMac 24" M3 (8 GB)Orin Nano~45Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~32Simulate output on iPhone 1720 GB
fits · 15.7 GB free · up to 16K ctx
1 GPU
RX 7900 XT~324Simulate output on RX 7900 XT32–40 GB
fits · 27.7 GB free · up to 16K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~787Simulate output on RTX 5090M5 Max36 GB~218Simulate output on Mac Studio M5 Max (36 GB)M532 GB~73Simulate output on MacBook Air 13" M5 (32 GB)Orin~90Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~59Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Deepseek Coder 1.3B Base?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
Here are all 4 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- MMLU
Epoch AI · Nov 2023Lab-reported
Lab-reported25.8%
#83/85 open
- GSM8K
Epoch AI · Nov 2023Lab-reported
Lab-reported4.4%
#62/62 open
- ARC Challenge
Epoch AI · Nov 2023Lab-reported
Lab-reported25.4%
#55/56 open
- WinoGrande
Epoch AI · Nov 2023Lab-reported
Lab-reported53.3%
#50/51 open
Alternatives to Deepseek Coder 1.3B Base
DeepSeek Coder family · 11 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MiniCPM OCR 1.0OpenBMB | — | 1.5 GB | ~580 | same size |
| Llama 3.2 1B InstructMeta | — | 2.3 GB | ~799 | same size |
| Inkling Small DSpark PreviewRadixArk | — | 1.5 GB | ~555 | same size |
| Sweep Next Edit 1.5BXenova | — | 1.6 GB | ~551 | same size |
| Ilama 3.2 1Bhmellor | — | 3.3 GB | ~231 | same size |
| T5gemma L L Ul2 ITGoogle | — | 4.3 GB | ~240 | same size |
Fine-tunes & variants(1)
Specifications
- Publisher
- DeepSeek
- Family
- DeepSeek Coder
- Parameters
- 1.3B
- Architecture
- LlamaForCausalLM
- Context Length
- 16,384 tokens
- Vocabulary Size
- 32,256
- Release Date
- 2023-10-28
- License
- Other
Frequently Asked Questions
How much VRAM does Deepseek Coder 1.3B Base need?
Deepseek Coder 1.3B Base requires 1.5 GB of VRAM at Q4_K_M, or 3.3 GB at BF16. Full 16K context adds up to 2.8 GB (4.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.3B × 4.8 bits ÷ 8 = 0.8 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 3.5 GB (at full 16K context)
VRAM usage by quantization
What's the best quantization for Deepseek Coder 1.3B Base?
For Deepseek Coder 1.3B Base, Q4_K_M (1.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Deepseek Coder 1.3B Base on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Deepseek Coder 1.3B Base. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run Deepseek Coder 1.3B Base locally?
Yes — Deepseek Coder 1.3B Base can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Deepseek Coder 1.3B Base?
At Q4_K_M, Deepseek Coder 1.3B Base can reach ~3243 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~443 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 1.48 × 0.65 = ~3514 tok/s
Estimated speed at Q4_K_M (1.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Deepseek Coder 1.3B Base?
At Q4_K_M, the download is about 0.78 GB. The full-precision BF16 version is 2.60 GB. The smallest option (Q2_K) is 0.55 GB.
Which GPUs can run Deepseek Coder 1.3B Base?
52 consumer GPUs can run Deepseek Coder 1.3B Base at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Deepseek Coder 1.3B Base?
163 devices with unified memory can run Deepseek Coder 1.3B Base at Q4_K_M (1.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.